Optical microlithography process, also known as photolithography process, has been widely used in semiconductor device manufacturing. The process includes duplicating or transferring circuit patterns from a template, commonly known as a photo-mask, onto semiconductor wafers. The circuit patterns on the photo-mask are typically represented by opaque, transparent, and/or semi-transparent regions. Patterns on the photo-mask template are then projected onto a photo-resistant coated wafer by ways of optical imaging through an exposure system.
Continuous advancement of Very Large Scale Integrated (VLSI) chip manufacturing technology to meet Moore's law of shrinking device dimensions in geometric progression has spurred the development of various Resolution Enhancement Techniques (RET) such as, for example, Optical Proximity Correction (OPC) methodology in optical microlithography. OPC is the method of choice for chip manufacturers in the foreseeable future due to its high volume yield in manufacturing and past history of success. Nevertheless, the ever shrinking device dimensions, combined with desires to enhance circuit performance in the deep sub-wavelength domain, require complex methodologies to ensure fidelity of mask patterns being transferred onto a printed wafer.
The ever increasing cost of mask manufacturing and inspection and the ever increasing complexity of OPC require that accuracy of a mask is correctly and properly verified before being manufactured. This is generally known as mask Manufacturability Verification (MV) or Printability Verification (PV). The primary focus of mask printability verification is to ensure accurate simulation, which means that the simulation shall not miss any real error on the mask. The cost of finding an error, when a mask is actually manufactured and particularly when it is being used in chip manufacturing, is known to be extremely high, which underlines the criticality of accuracy in the printability verification simulation process.
The accuracy of printability verification simulation generally depends on the accuracy and predictability of the underlying computer simulation model that represents the optical lithography process. The model for optical lithographic process is usually calibrated based on empirical data obtained from measurements of a set of test structures which are exposed under a single set of nominal exposure(s) or multiple set of exposures of variations of the nominal exposure conditions. The lithographic process model may generally include at least an optical model and a resist model, designed to capture various physical and/or chemical effects associated with the lithographic process.
An optical model typically represents the optical part of the lithographic process and may include effects from, for example, optical source, lithographic mask, and other optical elements such as a collection of lenses. Optical models used in printability verification are typically the same models as those used in OPC such as a Model-Based OPC (MBOPC). These models are in general related to a Sum of Coherent Source (SOCS) method, which is an algorithm for efficient computation of bilinear transform of Hopkins integral, to be described below in more details.
A resist model is usually created by statistically fitting mathematical equations to the empirical data. The resist parameters used in the model are often dependent on image traits such as minimum image intensity (Imin); maximum image intensity (Imax); slope of the image intensity (Islope); curvature of the image intensity (Icurve); etc. There are other properties such as image and pattern density which are also used in calibrating the image parameters.
Since the resist model and ultimately the lithographic process model are based upon the empirical data, their accuracy for predicting an image at a particular point depends on the proximity of that particular point, and parameters associated therewith, to the data used in the calibration or fitting. The accuracy of predication generally increases when the point under evaluation is near and within bounds of the fitted data. The accuracy of predication diminishes when the point under evaluation is farther and farther away from the fitted data.
In the state of art, printability verification is done without checking the accuracy of the predictability of the simulating model. In other words, same simulation accuracy is generally assumed over the whole mask layout irrespective of whether process parameters, which may be referred to as image parameters hereinafter, generated during verification are within the bounds of their predictability. The process of printability verification tries to determine the existence of errors in printability, identify such errors, and ensure that errors are not missed as a result of less accurate simulations.
Since calibration of the lithographic process model is generally based upon standard test structures that cannot exclusively represent all the complex scenarios appearing on real mask layouts, predictability of certain results may be extended from existing calibration data by applying interpolation and/or extrapolation. Generally, evaluation of an image tends to become less predictable in places where the results are extrapolated. Printability Verifications need to be aware of where results are less predictable.
In view of the above, there is a need in the art for improving the existing models and processes of performing mask printability verification such that no costly errors are missed.